{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55c745f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0adb905e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda3\\envs\\tensorflow_env\\lib\\importlib\\_bootstrap.py:205: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import tree\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "89ea572a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电量趋势下降指标</th>\n",
       "      <th>线损指标</th>\n",
       "      <th>告警类指标</th>\n",
       "      <th>是否窃漏电</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>9</td>\n",
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       "      <th>4</th>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     电量趋势下降指标  线损指标  告警类指标  是否窃漏电\n",
       "0           4     1      1      1\n",
       "1           4     0      4      1\n",
       "2           2     1      1      1\n",
       "3           9     0      0      0\n",
       "4           3     1      0      0\n",
       "..        ...   ...    ...    ...\n",
       "286         4     1      2      0\n",
       "287         1     0      2      0\n",
       "288         5     1      2      1\n",
       "289         2     1      0      0\n",
       "290         4     1      0      0\n",
       "\n",
       "[291 rows x 4 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_excel('dataset/dataset.xls')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f41de743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "286    0\n",
       "287    0\n",
       "288    1\n",
       "289    0\n",
       "290    0\n",
       "Name: 是否窃漏电, Length: 291, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=data.iloc[:,-1]\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "386e2bdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电量趋势下降指标</th>\n",
       "      <th>线损指标</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>4</td>\n",
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       "      <th>287</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>288</th>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>291 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     电量趋势下降指标  线损指标\n",
       "0           4     1\n",
       "1           4     0\n",
       "2           2     1\n",
       "3           9     0\n",
       "4           3     1\n",
       "..        ...   ...\n",
       "286         4     1\n",
       "287         1     0\n",
       "288         5     1\n",
       "289         2     1\n",
       "290         4     1\n",
       "\n",
       "[291 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=data.iloc[:,:-2]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f164460e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test =train_test_split(X,Y,test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5261e3ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(     电量趋势下降指标  线损指标\n",
       " 258         0     1\n",
       " 86          2     0\n",
       " 229         4     0\n",
       " 92          5     1\n",
       " 97          1     0\n",
       " ..        ...   ...\n",
       " 188         1     0\n",
       " 71          4     1\n",
       " 106         1     0\n",
       " 270         4     0\n",
       " 102         3     0\n",
       " \n",
       " [203 rows x 2 columns],      电量趋势下降指标  线损指标\n",
       " 84          2     1\n",
       " 259         0     0\n",
       " 45          2     1\n",
       " 176         4     0\n",
       " 143         5     1\n",
       " ..        ...   ...\n",
       " 137         2     0\n",
       " 111         4     0\n",
       " 208         4     1\n",
       " 152         2     1\n",
       " 18          8     1\n",
       " \n",
       " [88 rows x 2 columns], 258    0\n",
       " 86     0\n",
       " 229    0\n",
       " 92     1\n",
       " 97     0\n",
       "       ..\n",
       " 188    0\n",
       " 71     0\n",
       " 106    0\n",
       " 270    0\n",
       " 102    0\n",
       " Name: 是否窃漏电, Length: 203, dtype: int64, 84     0\n",
       " 259    0\n",
       " 45     1\n",
       " 176    0\n",
       " 143    0\n",
       "       ..\n",
       " 137    0\n",
       " 111    0\n",
       " 208    0\n",
       " 152    0\n",
       " 18     1\n",
       " Name: 是否窃漏电, Length: 88, dtype: int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1b81a1ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8863636363636364"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion=\"gini\")\n",
    "clf = clf.fit(X_train, y_train)\n",
    "clf.score(X_test, y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e14d28fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77264231, 0.22735769])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看特征的重要程度\n",
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1545878f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3e07749b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[71,  2],\n",
       "       [ 8,  7]], dtype=int64)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#混淆矩阵\n",
    "metrics.confusion_matrix(y_test,clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dc5d87b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8863636363636364"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算预测准确率\n",
    "# metrics.precision_score(y_test,clf.predict(X_test))\n",
    "metrics.accuracy_score(y_test,clf.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "07f0afd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.97560976, 0.90243902, 0.90243902, 0.9       , 0.9       ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#进行交叉验证\n",
    "scores=cross_val_score(clf,X_train, y_train,cv=5,scoring='accuracy')#\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "30b5d8e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9160975609756097"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#取5折交叉验证的平均值\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7ef39c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "#画ROC曲线\n",
    "y_score =clf.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2a5ff58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7196347031963471"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test,y_score)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "43608de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "lw = 2\n",
    "plt.plot(fpr, tpr, color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13d595b9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
